This notebook is an exercise in the Geospatial Analysis course. You can reference the tutorial at this link.

Introduction

You are part of a crisis response team, and you want to identify how hospitals have been responding to crash collisions in New York City.

Before you get started, run the code cell below to set everything up.

import math
import geopandas as gpd
import pandas as pd
from shapely.geometry import MultiPolygon

import folium
from folium import Choropleth, Marker
from folium.plugins import HeatMap, MarkerCluster

#from learntools.core import binder
#binder.bind(globals())
#from learntools.geospatial.ex5 import *

You’ll use the embed_map() function to visualize your maps.

def embed_map(m, file_name):
    from IPython.display import IFrame
    m.save(file_name)
    return IFrame(file_name, width='100%', height='500px')

Exercises

1) Visualize the collision data.

Run the code cell below to load a GeoDataFrame collisions tracking major motor vehicle collisions in 2013-2018.

collisions = gpd.read_file("C:/Users/ENGLISH/Desktop/archive/NYPD_Motor_Vehicle_Collisions/NYPD_Motor_Vehicle_Collisions/NYPD_Motor_Vehicle_Collisions.shp")
collisions.head()

Use the “LATITUDE” and “LONGITUDE” columns to create an interactive map to visualize the collision data. What type of map do you think is most effective?

m_1 = folium.Map(location=[40.7, -74], zoom_start=11) 

# Your code here: Visualize the collision data
HeatMap(data = collisions[["LATITUDE", "LONGITUDE"]], radius = 9).add_to(m_1)

# Uncomment to see a hint
#q_1.hint()

# Show the map
m_1

Make this Notebook Trusted to load map: File -> Trust Notebook

# Get credit for your work after you have created a map
#q_1.check()

# Uncomment to see our solution (your code may look different!)
#q_1.solution()

2) Understand hospital coverage.

Run the next code cell to load the hospital data.

hospitals = gpd.read_file(r"C:\Users\ENGLISH\Desktop\archive\nyu_2451_34494\nyu_2451_34494\nyu_2451_34494.shp")
hospitals.head()

	id	name	address	zip	factype	facname	capacity	capname	bcode	xcoord	ycoord	latitude	longitude	geometry
0	317000001H1178	BRONX-LEBANON HOSPITAL CENTER - CONCOURSE DIVI...	1650 Grand Concourse	10457	3102	Hospital	415	Beds	36005	1008872.0	246596.0	40.843490	-73.911010	POINT (1008872.000 246596.000)
1	317000001H1164	BRONX-LEBANON HOSPITAL CENTER - FULTON DIVISION	1276 Fulton Ave	10456	3102	Hospital	164	Beds	36005	1011044.0	242204.0	40.831429	-73.903178	POINT (1011044.000 242204.000)
2	317000011H1175	CALVARY HOSPITAL INC	1740-70 Eastchester Rd	10461	3102	Hospital	225	Beds	36005	1027505.0	248287.0	40.848060	-73.843656	POINT (1027505.000 248287.000)
3	317000002H1165	JACOBI MEDICAL CENTER	1400 Pelham Pkwy	10461	3102	Hospital	457	Beds	36005	1027042.0	251065.0	40.855687	-73.845311	POINT (1027042.000 251065.000)
4	317000008H1172	LINCOLN MEDICAL & MENTAL HEALTH CENTER	234 E 149 St	10451	3102	Hospital	362	Beds	36005	1005154.0	236853.0	40.816758	-73.924478	POINT (1005154.000 236853.000)

Use the “latitude” and “longitude” columns to visualize the hospital locations.

m_2 = folium.Map(location=[40.7, -74], zoom_start=11) 

# Your code here: Visualize the hospital locations
for idx, row in hospitals.iterrows():
    Marker([row['latitude'], row['longitude']], popup=row['name']).add_to(m_2)

# Uncomment to see a hint
#q_2.hint()
        
# Show the map
m_2

Make this Notebook Trusted to load map: File -> Trust Notebook

# Get credit for your work after you have created a map
#q_2.check()

# Uncomment to see our solution (your code may look different!)
#q_2.solution()

3) When was the closest hospital more than 10 kilometers away?

Create a DataFrame outside_range containing all rows from collisions with crashes that occurred more than 10 kilometers from the closest hospital.

Note that both hospitals and collisions have EPSG 2263 as the coordinate reference system, and EPSG 2263 has units of meters.

# Your code here
h_p = gpd.GeoDataFrame(geometry=hospitals.geometry).buffer(10000)
u = h_p.geometry.unary_union
outside_range = collisions.loc[~collisions["geometry"].apply(lambda x: u.contains(x))]

# Check your answer
#q_3.check()

# Lines below will give you a hint or solution code
#q_3.hint()
#q_3.solution()

The next code cell calculates the percentage of collisions that occurred more than 10 kilometers away from the closest hospital.

percentage = round(100*len(outside_range)/len(collisions), 2)
print("Percentage of collisions more than 10 km away from the closest hospital: {}%".format(percentage))

Percentage of collisions more than 10 km away from the closest hospital: 15.12%

4) Make a recommender.

When collisions occur in distant locations, it becomes even more vital that injured persons are transported to the nearest available hospital.

With this in mind, you decide to create a recommender that: - takes the location of the crash (in EPSG 2263) as input, - finds the closest hospital (where distance calculations are done in EPSG 2263), and - returns the name of the closest hospital.

def best_hospital(collision_location):
    m = hospitals.geometry.distance(collision_location).idxmin()
    hp = hospitals.iloc[m]
    name = hp["name"]
    return name

# Test your function: this should suggest CALVARY HOSPITAL INC
print(best_hospital(outside_range.geometry.iloc[0]))

# Check your answer
#q_4.check()

CALVARY HOSPITAL INC

# Lines below will give you a hint or solution code
#q_4.hint()
#q_4.solution()

5) Which hospital is under the highest demand?

Considering only collisions in the outside_range DataFrame, which hospital is most recommended?

Your answer should be a Python string that exactly matches the name of the hospital returned by the function you created in 4).

# Your code here
#highest_demand 

# Check your answer
#q_5.check()

# Lines below will give you a hint or solution code
#q_5.hint()
#q_5.solution()

6) Where should the city construct new hospitals?

Run the next code cell (without changes) to visualize hospital locations, in addition to collisions that occurred more than 10 kilometers away from the closest hospital.

m_6 = folium.Map(location=[40.7, -74], zoom_start=11) 

coverage = gpd.GeoDataFrame(geometry=hospitals.geometry).buffer(10000)
folium.GeoJson(coverage.geometry.to_crs(epsg=4326)).add_to(m_6)
HeatMap(data=outside_range[['LATITUDE', 'LONGITUDE']], radius=9).add_to(m_6)
folium.LatLngPopup().add_to(m_6)

m_6

Make this Notebook Trusted to load map: File -> Trust Notebook

Click anywhere on the map to see a pop-up with the corresponding location in latitude and longitude.

The city of New York reaches out to you for help with deciding locations for two brand new hospitals. They specifically want your help with identifying locations to bring the calculated percentage from step 3) to less than ten percent. Using the map (and without worrying about zoning laws or what potential buildings would have to be removed in order to build the hospitals), can you identify two locations that would help the city accomplish this goal?

Put the proposed latitude and longitude for hospital 1 in lat_1 and long_1, respectively. (Likewise for hospital 2.)

Then, run the rest of the cell as-is to see the effect of the new hospitals. Your answer will be marked correct, if the two new hospitals bring the percentage to less than ten percent.

# Your answer here: proposed location of hospital 1
lat_1 = 40.7
long_1 = -74

# Your answer here: proposed location of hospital 2
lat_2 = 40.7
long_2 = -74


# Do not modify the code below this line
try:
    new_df = pd.DataFrame(
        {'Latitude': [lat_1, lat_2],
         'Longitude': [long_1, long_2]})
    new_gdf = gpd.GeoDataFrame(new_df, geometry=gpd.points_from_xy(new_df.Longitude, new_df.Latitude))
    new_gdf.crs = {'init' :'epsg:4326'}
    new_gdf = new_gdf.to_crs(epsg=2263)
    # get new percentage
    new_coverage = gpd.GeoDataFrame(geometry=new_gdf.geometry).buffer(10000)
    new_my_union = new_coverage.geometry.unary_union
    new_outside_range = outside_range.loc[~outside_range["geometry"].apply(lambda x: new_my_union.contains(x))]
    new_percentage = round(100*len(new_outside_range)/len(collisions), 2)
    print("(NEW) Percentage of collisions more than 10 km away from the closest hospital: {}%".format(new_percentage))
    
    # make the map
    m = folium.Map(location=[40.7, -74], zoom_start=11) 
    folium.GeoJson(coverage.geometry.to_crs(epsg=4326)).add_to(m)
    folium.GeoJson(new_coverage.geometry.to_crs(epsg=4326)).add_to(m)
    for idx, row in new_gdf.iterrows():
        Marker([row['Latitude'], row['Longitude']]).add_to(m)
    HeatMap(data=new_outside_range[['LATITUDE', 'LONGITUDE']], radius=9).add_to(m)
    folium.LatLngPopup().add_to(m)
    display(m)
except:
    0

c:\Users\ENGLISH\.conda\envs\myenv\Lib\site-packages\pyproj\crs\crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6
  in_crs_string = _prepare_from_proj_string(in_crs_string)

(NEW) Percentage of collisions more than 10 km away from the closest hospital: 15.12%

Make this Notebook Trusted to load map: File -> Trust Notebook

# Uncomment to see one potential answer 
#q_6.solution()

Congratulations!

You have just completed the Geospatial Analysis micro-course! Great job!

Have questions or comments? Visit the course discussion forum to chat with other learners.